This section empirically demonstrates the
practicality and flexibility of RCPMs.
We consider synthetic manifold learning tasks
similar to [1]}, [2]} on
both spheres and tori, and a real-life application over the sphere.
We cover the different use cases of RCPMs: density estimation,
mapping estimation and geodesic transport.
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